Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices
The recurring neural network is a deep learning algorithm that is commonly used to develop prediction systems. There are many variants of RNN such as RNN itself, long-short-term memory (LSTM), and gated recurring unit, so it is frequently debatable which algorithm from the RNN family has the most op...
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Ikatan Ahli Informatika Indonesia
2024-06-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5810 |
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| author | Zidane Ikkoy Ramadhan Harya Widiputra |
| author_facet | Zidane Ikkoy Ramadhan Harya Widiputra |
| author_sort | Zidane Ikkoy Ramadhan |
| collection | DOAJ |
| description | The recurring neural network is a deep learning algorithm that is commonly used to develop prediction systems. There are many variants of RNN such as RNN itself, long-short-term memory (LSTM), and gated recurring unit, so it is frequently debatable which algorithm from the RNN family has the most optimal efficiency and computation time. When developing a prediction system, sequential or time series data is required so that an accurate prediction can be made. Sequential or time series data involve data arranged in a time sequence, such as weather data, financial data, carbon emission data, and traffic data recorded over time. This research will be carried out by predicting the three RNN models against historical Bitcoin value data. The research method used is Experimental Design by comparing the performance between the three models on bitcoin value time series data, testing is done by involving hyperparameters such as Tanh, Sigmoid, and ReLU activation functions, batch size, and epochs. The aim of this research is to find out which RNN model can produce the most optimal performance and find out what performance measures can be used to evaluate and compare the performance between the three models. The results of the study show that LSTM is the most effective model with RMSE 0.012441 and MSE 0.000155 but inefficient because it takes 3 minutes 24 seconds to run the computation; in the meantime, the Tanh activation function gives the most optimal prediction than Sigmoid and RelU and therefore should be the main candidate to be used with RNN models when predicting Bitcoin prices. |
| format | Article |
| id | doaj-art-67ff9a0526b4425aadfb7751028623cd |
| institution | Kabale University |
| issn | 2580-0760 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-67ff9a0526b4425aadfb7751028623cd2025-01-13T03:33:46ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602024-06-018337738810.29207/resti.v8i3.58105810Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin PricesZidane Ikkoy Ramadhan0Harya Widiputra1Perbanas InstitutePerbanas InstituteThe recurring neural network is a deep learning algorithm that is commonly used to develop prediction systems. There are many variants of RNN such as RNN itself, long-short-term memory (LSTM), and gated recurring unit, so it is frequently debatable which algorithm from the RNN family has the most optimal efficiency and computation time. When developing a prediction system, sequential or time series data is required so that an accurate prediction can be made. Sequential or time series data involve data arranged in a time sequence, such as weather data, financial data, carbon emission data, and traffic data recorded over time. This research will be carried out by predicting the three RNN models against historical Bitcoin value data. The research method used is Experimental Design by comparing the performance between the three models on bitcoin value time series data, testing is done by involving hyperparameters such as Tanh, Sigmoid, and ReLU activation functions, batch size, and epochs. The aim of this research is to find out which RNN model can produce the most optimal performance and find out what performance measures can be used to evaluate and compare the performance between the three models. The results of the study show that LSTM is the most effective model with RMSE 0.012441 and MSE 0.000155 but inefficient because it takes 3 minutes 24 seconds to run the computation; in the meantime, the Tanh activation function gives the most optimal prediction than Sigmoid and RelU and therefore should be the main candidate to be used with RNN models when predicting Bitcoin prices.https://jurnal.iaii.or.id/index.php/RESTI/article/view/5810deep learningrecurrent neural networklstmgrubitcoin |
| spellingShingle | Zidane Ikkoy Ramadhan Harya Widiputra Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) deep learning recurrent neural network lstmgru bitcoin |
| title | Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices |
| title_full | Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices |
| title_fullStr | Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices |
| title_full_unstemmed | Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices |
| title_short | Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices |
| title_sort | comparative analysis of recurrent neural network models performance in predicting bitcoin prices |
| topic | deep learning recurrent neural network lstmgru bitcoin |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/5810 |
| work_keys_str_mv | AT zidaneikkoyramadhan comparativeanalysisofrecurrentneuralnetworkmodelsperformanceinpredictingbitcoinprices AT haryawidiputra comparativeanalysisofrecurrentneuralnetworkmodelsperformanceinpredictingbitcoinprices |